{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to build a linear factor model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algorithmic trading strategies use linear factor models to quantify the relationship between the return of an asset and the sources of risk that represent the main drivers of these returns. Each factor risk carries a premium, and the total asset return can be expected to correspond to a weighted average of these risk premia."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several practical applications of factor models across the portfolio management process from construction and asset selection to risk management and performance evaluation. The importance of factor models continues to grow as common risk factors are now tradeable:\n",
    "\n",
    "- A summary of the returns of many assets by a much smaller number of factors reduces the amount of data required to estimate the covariance matrix when optimizing a portfolio\n",
    "- An estimate of the exposure of an asset or a portfolio to these factors allows for the management of the resultant risk, for instance by entering suitable hedges when risk factors are themselves traded\n",
    "- A factor model also permits the assessment of the incremental signal content of new alpha factors\n",
    "- A factor model can also help assess whether a manager's performance relative to a benchmark is indeed due to skill in selecting assets and timing the market, or if instead, the performance can be explained by portfolio tilts towards known return drivers that can today be replicated as low-cost, passively managed funds without incurring active management fees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:08.177079Z",
     "start_time": "2020-06-17T18:12:08.170608Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:10.409217Z",
     "start_time": "2020-06-17T18:12:08.179878Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from statsmodels.api import OLS, add_constant\n",
    "import pandas_datareader.data as web\n",
    "\n",
    "from linearmodels.asset_pricing import LinearFactorModel\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:10.413517Z",
     "start_time": "2020-06-17T18:12:10.411050Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fama and French make updated risk factor and research portfolio data available through their [website](http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html), and you can use the `pandas_datareader` package to obtain the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Risk Factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In particular, we will be using the five Fama—French factors that result from sorting stocks first into three size groups and then into two for each of the remaining three firm-specific factors. \n",
    "\n",
    "Hence, the factors involve three sets of value-weighted portfolios formed as 3 x 2 sorts on size and book-to-market, size and operating profitability, and size and investment. The risk factor values computed as the average returns of the portfolios (PF) as outlined in the following table:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Label | Name                          | Description                                                                                                                                                                               |\n",
    "|-------|-------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
    "| SMB   | Small Minus Big               | Average return on the nine small stock portfolios minus the average return on the nine big stock portfolios                                                                               |\n",
    "| HML   | High Minus Low                | Average return on the two value portfolios minus the average return on the two growth portfolios                                                                                          |\n",
    "| RMW   | Robust minus Weak             | Average return on the two robust operating profitability portfolios minus the average return on the two weak operating profitability portfolios                                           |\n",
    "| CMA   | Conservative Minus Aggressive | Average return on the two conservative investment portfolios minus the average return on the two aggressive investment portfolios                                                         |\n",
    "| Rm-Rf | Excess return on the market   | Value-weight return of all firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ at the beginning of month t with 'good' data for t minus the one-month Treasury bill rate |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Fama-French 5 factors are based on the 6 value-weight portfolios formed on size and book-to-market, the 6 value-weight portfolios formed on size and operating profitability, and the 6 value-weight portfolios formed on size and investment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use returns at a monthly frequency that we obtain for the period 2010 – 2017 as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:10.553117Z",
     "start_time": "2020-06-17T18:12:10.415276Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 96 entries, 2010-01 to 2017-12\n",
      "Freq: M\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Mkt-RF  96 non-null     float64\n",
      " 1   SMB     96 non-null     float64\n",
      " 2   HML     96 non-null     float64\n",
      " 3   RMW     96 non-null     float64\n",
      " 4   CMA     96 non-null     float64\n",
      " 5   RF      96 non-null     float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 5.2 KB\n"
     ]
    }
   ],
   "source": [
    "ff_factor = 'F-F_Research_Data_5_Factors_2x3'\n",
    "ff_factor_data = web.DataReader(ff_factor, 'famafrench', start='2010', end='2017-12')[0]\n",
    "ff_factor_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:10.616778Z",
     "start_time": "2020-06-17T18:12:10.554498Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mkt-RF</th>\n",
       "      <th>SMB</th>\n",
       "      <th>HML</th>\n",
       "      <th>RMW</th>\n",
       "      <th>CMA</th>\n",
       "      <th>RF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.158438</td>\n",
       "      <td>0.053958</td>\n",
       "      <td>-0.064896</td>\n",
       "      <td>0.147396</td>\n",
       "      <td>0.049271</td>\n",
       "      <td>0.012604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.579997</td>\n",
       "      <td>2.296530</td>\n",
       "      <td>2.199398</td>\n",
       "      <td>1.551005</td>\n",
       "      <td>1.408406</td>\n",
       "      <td>0.022583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-7.890000</td>\n",
       "      <td>-4.550000</td>\n",
       "      <td>-4.500000</td>\n",
       "      <td>-3.990000</td>\n",
       "      <td>-3.330000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.917500</td>\n",
       "      <td>-1.600000</td>\n",
       "      <td>-1.512500</td>\n",
       "      <td>-1.032500</td>\n",
       "      <td>-0.962500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.235000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>-0.295000</td>\n",
       "      <td>0.155000</td>\n",
       "      <td>-0.015000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.190000</td>\n",
       "      <td>1.505000</td>\n",
       "      <td>1.140000</td>\n",
       "      <td>1.145000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.350000</td>\n",
       "      <td>6.810000</td>\n",
       "      <td>8.290000</td>\n",
       "      <td>3.480000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>0.090000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Mkt-RF        SMB        HML        RMW        CMA         RF\n",
       "count  96.000000  96.000000  96.000000  96.000000  96.000000  96.000000\n",
       "mean    1.158438   0.053958  -0.064896   0.147396   0.049271   0.012604\n",
       "std     3.579997   2.296530   2.199398   1.551005   1.408406   0.022583\n",
       "min    -7.890000  -4.550000  -4.500000  -3.990000  -3.330000   0.000000\n",
       "25%    -0.917500  -1.600000  -1.512500  -1.032500  -0.962500   0.000000\n",
       "50%     1.235000   0.180000  -0.295000   0.155000  -0.015000   0.000000\n",
       "75%     3.190000   1.505000   1.140000   1.145000   0.920000   0.010000\n",
       "max    11.350000   6.810000   8.290000   3.480000   3.700000   0.090000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ff_factor_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Portfolios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fama and French also make available numerous portfolios that we can illustrate the estimation of the factor exposures, as well as the value of the risk premia available in the market for a given time period. We will use a panel of the 17 industry portfolios at a monthly frequency. \n",
    "\n",
    "We will subtract the risk-free rate from the returns because the factor model works with excess returns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:11.129492Z",
     "start_time": "2020-06-17T18:12:10.623727Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 96 entries, 2010-01 to 2017-12\n",
      "Freq: M\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Food    96 non-null     float64\n",
      " 1   Mines   96 non-null     float64\n",
      " 2   Oil     96 non-null     float64\n",
      " 3   Clths   96 non-null     float64\n",
      " 4   Durbl   96 non-null     float64\n",
      " 5   Chems   96 non-null     float64\n",
      " 6   Cnsum   96 non-null     float64\n",
      " 7   Cnstr   96 non-null     float64\n",
      " 8   Steel   96 non-null     float64\n",
      " 9   FabPr   96 non-null     float64\n",
      " 10  Machn   96 non-null     float64\n",
      " 11  Cars    96 non-null     float64\n",
      " 12  Trans   96 non-null     float64\n",
      " 13  Utils   96 non-null     float64\n",
      " 14  Rtail   96 non-null     float64\n",
      " 15  Finan   96 non-null     float64\n",
      " 16  Other   96 non-null     float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 13.5 KB\n"
     ]
    }
   ],
   "source": [
    "ff_portfolio = '17_Industry_Portfolios'\n",
    "ff_portfolio_data = web.DataReader(ff_portfolio, 'famafrench', start='2010', end='2017-12')[0]\n",
    "ff_portfolio_data = ff_portfolio_data.sub(ff_factor_data.RF, axis=0)\n",
    "ff_portfolio_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:11.258222Z",
     "start_time": "2020-06-17T18:12:11.135101Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food</th>\n",
       "      <th>Mines</th>\n",
       "      <th>Oil</th>\n",
       "      <th>Clths</th>\n",
       "      <th>Durbl</th>\n",
       "      <th>Chems</th>\n",
       "      <th>Cnsum</th>\n",
       "      <th>Cnstr</th>\n",
       "      <th>Steel</th>\n",
       "      <th>FabPr</th>\n",
       "      <th>Machn</th>\n",
       "      <th>Cars</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Utils</th>\n",
       "      <th>Rtail</th>\n",
       "      <th>Finan</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.045625</td>\n",
       "      <td>0.203229</td>\n",
       "      <td>0.547917</td>\n",
       "      <td>1.396979</td>\n",
       "      <td>1.154896</td>\n",
       "      <td>1.303438</td>\n",
       "      <td>1.136250</td>\n",
       "      <td>1.731250</td>\n",
       "      <td>0.555625</td>\n",
       "      <td>1.351042</td>\n",
       "      <td>1.227604</td>\n",
       "      <td>1.278854</td>\n",
       "      <td>1.465521</td>\n",
       "      <td>0.891250</td>\n",
       "      <td>1.234375</td>\n",
       "      <td>1.243646</td>\n",
       "      <td>1.281979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.795857</td>\n",
       "      <td>7.902683</td>\n",
       "      <td>5.577552</td>\n",
       "      <td>5.025167</td>\n",
       "      <td>5.137095</td>\n",
       "      <td>5.594722</td>\n",
       "      <td>3.174283</td>\n",
       "      <td>5.246562</td>\n",
       "      <td>7.389824</td>\n",
       "      <td>4.694688</td>\n",
       "      <td>4.811242</td>\n",
       "      <td>5.718887</td>\n",
       "      <td>4.151203</td>\n",
       "      <td>3.237306</td>\n",
       "      <td>3.508655</td>\n",
       "      <td>4.808350</td>\n",
       "      <td>3.710443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.170000</td>\n",
       "      <td>-24.380000</td>\n",
       "      <td>-12.010000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-13.210000</td>\n",
       "      <td>-17.390000</td>\n",
       "      <td>-7.300000</td>\n",
       "      <td>-13.960000</td>\n",
       "      <td>-20.490000</td>\n",
       "      <td>-11.960000</td>\n",
       "      <td>-9.080000</td>\n",
       "      <td>-11.650000</td>\n",
       "      <td>-8.560000</td>\n",
       "      <td>-6.990000</td>\n",
       "      <td>-9.180000</td>\n",
       "      <td>-11.020000</td>\n",
       "      <td>-7.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.785000</td>\n",
       "      <td>-5.832500</td>\n",
       "      <td>-3.167500</td>\n",
       "      <td>-1.865000</td>\n",
       "      <td>-2.017500</td>\n",
       "      <td>-1.445000</td>\n",
       "      <td>-0.920000</td>\n",
       "      <td>-2.462500</td>\n",
       "      <td>-4.410000</td>\n",
       "      <td>-1.447500</td>\n",
       "      <td>-2.047500</td>\n",
       "      <td>-1.245000</td>\n",
       "      <td>-0.880000</td>\n",
       "      <td>-0.745000</td>\n",
       "      <td>-0.962500</td>\n",
       "      <td>-1.447500</td>\n",
       "      <td>-1.067500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.930000</td>\n",
       "      <td>-0.415000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>1.160000</td>\n",
       "      <td>1.205000</td>\n",
       "      <td>1.435000</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>2.190000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>1.485000</td>\n",
       "      <td>1.545000</td>\n",
       "      <td>0.645000</td>\n",
       "      <td>1.505000</td>\n",
       "      <td>1.215000</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>1.940000</td>\n",
       "      <td>1.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.187500</td>\n",
       "      <td>5.707500</td>\n",
       "      <td>3.915000</td>\n",
       "      <td>3.857500</td>\n",
       "      <td>4.315000</td>\n",
       "      <td>4.442500</td>\n",
       "      <td>3.317500</td>\n",
       "      <td>5.390000</td>\n",
       "      <td>4.220000</td>\n",
       "      <td>3.875000</td>\n",
       "      <td>4.657500</td>\n",
       "      <td>4.802500</td>\n",
       "      <td>4.227500</td>\n",
       "      <td>2.965000</td>\n",
       "      <td>3.355000</td>\n",
       "      <td>4.052500</td>\n",
       "      <td>3.517500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.670000</td>\n",
       "      <td>21.920000</td>\n",
       "      <td>16.300000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>16.580000</td>\n",
       "      <td>18.370000</td>\n",
       "      <td>8.290000</td>\n",
       "      <td>15.550000</td>\n",
       "      <td>21.350000</td>\n",
       "      <td>17.660000</td>\n",
       "      <td>14.650000</td>\n",
       "      <td>20.860000</td>\n",
       "      <td>13.160000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>12.360000</td>\n",
       "      <td>13.430000</td>\n",
       "      <td>10.790000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Food       Mines      Oil        Clths      Durbl      Chems  \\\n",
       "count  96.000000  96.000000  96.000000  96.000000  96.000000  96.000000   \n",
       "mean    1.045625   0.203229   0.547917   1.396979   1.154896   1.303438   \n",
       "std     2.795857   7.902683   5.577552   5.025167   5.137095   5.594722   \n",
       "min    -5.170000 -24.380000 -12.010000 -10.000000 -13.210000 -17.390000   \n",
       "25%    -0.785000  -5.832500  -3.167500  -1.865000  -2.017500  -1.445000   \n",
       "50%     0.930000  -0.415000   1.040000   1.160000   1.205000   1.435000   \n",
       "75%     3.187500   5.707500   3.915000   3.857500   4.315000   4.442500   \n",
       "max     6.670000  21.920000  16.300000  17.200000  16.580000  18.370000   \n",
       "\n",
       "           Cnsum      Cnstr      Steel      FabPr      Machn      Cars   \\\n",
       "count  96.000000  96.000000  96.000000  96.000000  96.000000  96.000000   \n",
       "mean    1.136250   1.731250   0.555625   1.351042   1.227604   1.278854   \n",
       "std     3.174283   5.246562   7.389824   4.694688   4.811242   5.718887   \n",
       "min    -7.300000 -13.960000 -20.490000 -11.960000  -9.080000 -11.650000   \n",
       "25%    -0.920000  -2.462500  -4.410000  -1.447500  -2.047500  -1.245000   \n",
       "50%     1.470000   2.190000   0.660000   1.485000   1.545000   0.645000   \n",
       "75%     3.317500   5.390000   4.220000   3.875000   4.657500   4.802500   \n",
       "max     8.290000  15.550000  21.350000  17.660000  14.650000  20.860000   \n",
       "\n",
       "           Trans      Utils      Rtail      Finan      Other  \n",
       "count  96.000000  96.000000  96.000000  96.000000  96.000000  \n",
       "mean    1.465521   0.891250   1.234375   1.243646   1.281979  \n",
       "std     4.151203   3.237306   3.508655   4.808350   3.710443  \n",
       "min    -8.560000  -6.990000  -9.180000 -11.020000  -7.920000  \n",
       "25%    -0.880000  -0.745000  -0.962500  -1.447500  -1.067500  \n",
       "50%     1.505000   1.215000   0.880000   1.940000   1.575000  \n",
       "75%     4.227500   2.965000   3.355000   4.052500   3.517500  \n",
       "max    13.160000   7.900000  12.360000  13.430000  10.790000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ff_portfolio_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Equity Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:19.665692Z",
     "start_time": "2020-06-17T18:12:11.263152Z"
    }
   },
   "outputs": [],
   "source": [
    "with pd.HDFStore('../data/assets.h5') as store:\n",
    "    prices = store['/quandl/wiki/prices'].adj_close.unstack().loc['2010':'2017']\n",
    "    equities = store['/us_equities/stocks'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:19.777005Z",
     "start_time": "2020-06-17T18:12:19.667038Z"
    }
   },
   "outputs": [],
   "source": [
    "sectors = equities.filter(prices.columns, axis=0).sector.to_dict()\n",
    "prices = prices.filter(sectors.keys()).dropna(how='all', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:19.981624Z",
     "start_time": "2020-06-17T18:12:19.783931Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 95 entries, 2010-02 to 2017-12\n",
      "Freq: M\n",
      "Columns: 1986 entries, A to ZUMZ\n",
      "dtypes: float64(1986)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "returns = prices.resample('M').last().pct_change().mul(100).to_period('M')\n",
    "returns = returns.dropna(how='all').dropna(axis=1)\n",
    "returns.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Align data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:19.991602Z",
     "start_time": "2020-06-17T18:12:19.986267Z"
    }
   },
   "outputs": [],
   "source": [
    "ff_factor_data = ff_factor_data.loc[returns.index]\n",
    "ff_portfolio_data = ff_portfolio_data.loc[returns.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:20.059887Z",
     "start_time": "2020-06-17T18:12:19.996970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mkt-RF</th>\n",
       "      <th>SMB</th>\n",
       "      <th>HML</th>\n",
       "      <th>RMW</th>\n",
       "      <th>CMA</th>\n",
       "      <th>RF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.206000</td>\n",
       "      <td>0.051474</td>\n",
       "      <td>-0.068737</td>\n",
       "      <td>0.163579</td>\n",
       "      <td>0.044947</td>\n",
       "      <td>0.012737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.568367</td>\n",
       "      <td>2.308583</td>\n",
       "      <td>2.210743</td>\n",
       "      <td>1.551063</td>\n",
       "      <td>1.415237</td>\n",
       "      <td>0.022665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-7.890000</td>\n",
       "      <td>-4.550000</td>\n",
       "      <td>-4.500000</td>\n",
       "      <td>-3.990000</td>\n",
       "      <td>-3.330000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.565000</td>\n",
       "      <td>-1.610000</td>\n",
       "      <td>-1.535000</td>\n",
       "      <td>-0.955000</td>\n",
       "      <td>-0.975000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.290000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>-0.300000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>-0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.260000</td>\n",
       "      <td>1.550000</td>\n",
       "      <td>1.140000</td>\n",
       "      <td>1.160000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.350000</td>\n",
       "      <td>6.810000</td>\n",
       "      <td>8.290000</td>\n",
       "      <td>3.480000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>0.090000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Mkt-RF        SMB        HML        RMW        CMA         RF\n",
       "count  95.000000  95.000000  95.000000  95.000000  95.000000  95.000000\n",
       "mean    1.206000   0.051474  -0.068737   0.163579   0.044947   0.012737\n",
       "std     3.568367   2.308583   2.210743   1.551063   1.415237   0.022665\n",
       "min    -7.890000  -4.550000  -4.500000  -3.990000  -3.330000   0.000000\n",
       "25%    -0.565000  -1.610000  -1.535000  -0.955000  -0.975000   0.000000\n",
       "50%     1.290000   0.150000  -0.300000   0.170000  -0.020000   0.000000\n",
       "75%     3.260000   1.550000   1.140000   1.160000   0.930000   0.010000\n",
       "max    11.350000   6.810000   8.290000   3.480000   3.700000   0.090000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ff_factor_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute excess Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:20.200403Z",
     "start_time": "2020-06-17T18:12:20.064500Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 95 entries, 2010-02 to 2017-12\n",
      "Freq: M\n",
      "Columns: 1986 entries, A to ZUMZ\n",
      "dtypes: float64(1986)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "excess_returns = returns.sub(ff_factor_data.RF, axis=0)\n",
    "excess_returns.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:20.239038Z",
     "start_time": "2020-06-17T18:12:20.204947Z"
    }
   },
   "outputs": [],
   "source": [
    "excess_returns = excess_returns.clip(lower=np.percentile(excess_returns, 1),\n",
    "                                     upper=np.percentile(excess_returns, 99))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fama-Macbeth Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given data on risk factors and portfolio returns, it is useful to estimate the portfolio's exposure, that is, how much the risk factors drive portfolio returns, as well as how much the exposure to a given factor is worth, that is, the what market's risk factor premium is. The risk premium then permits to estimate the return for any portfolio provided the factor exposure is known or can be assumed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:20.267198Z",
     "start_time": "2020-06-17T18:12:20.246337Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 95 entries, 2010-02 to 2017-12\n",
      "Freq: M\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Food    95 non-null     float64\n",
      " 1   Mines   95 non-null     float64\n",
      " 2   Oil     95 non-null     float64\n",
      " 3   Clths   95 non-null     float64\n",
      " 4   Durbl   95 non-null     float64\n",
      " 5   Chems   95 non-null     float64\n",
      " 6   Cnsum   95 non-null     float64\n",
      " 7   Cnstr   95 non-null     float64\n",
      " 8   Steel   95 non-null     float64\n",
      " 9   FabPr   95 non-null     float64\n",
      " 10  Machn   95 non-null     float64\n",
      " 11  Cars    95 non-null     float64\n",
      " 12  Trans   95 non-null     float64\n",
      " 13  Utils   95 non-null     float64\n",
      " 14  Rtail   95 non-null     float64\n",
      " 15  Finan   95 non-null     float64\n",
      " 16  Other   95 non-null     float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 13.4 KB\n"
     ]
    }
   ],
   "source": [
    "ff_portfolio_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:20.293304Z",
     "start_time": "2020-06-17T18:12:20.273422Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 95 entries, 2010-02 to 2017-12\n",
      "Freq: M\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Mkt-RF  95 non-null     float64\n",
      " 1   SMB     95 non-null     float64\n",
      " 2   HML     95 non-null     float64\n",
      " 3   RMW     95 non-null     float64\n",
      " 4   CMA     95 non-null     float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 4.5 KB\n"
     ]
    }
   ],
   "source": [
    "ff_factor_data = ff_factor_data.drop('RF', axis=1)\n",
    "ff_factor_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To address the inference problem caused by the correlation of the residuals, Fama and MacBeth proposed a two-step methodology for a cross-sectional regression of returns on factors. The two-stage Fama—Macbeth regression is designed to estimate the premium rewarded for the exposure to a particular risk factor by the market. The two stages consist of:\n",
    "\n",
    "- First stage: N time-series regression, one for each asset or portfolio, of its excess returns on the factors to estimate the factor loadings.\n",
    "\n",
    "- Second stage: T cross-sectional regression, one for each time period, to estimate the risk premium.\n",
    "\n",
    "See corresponding section in Chapter 7 of [Machine Learning for Trading](https://www.amazon.com/Hands-Machine-Learning-Algorithmic-Trading-ebook/dp/B07JLFH7C5/ref=sr_1_2?ie=UTF8&qid=1548455634&sr=8-2&keywords=machine+learning+algorithmic+trading) for details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compute the factor risk premia as the time average and get t-statistic to assess their individual significance, using the assumption that the risk premia estimates are independent over time.\n",
    "\n",
    "If we had a very large and representative data sample on traded risk factors we could use the sample mean as a risk premium estimate. However, we typically do not have a sufficiently long history to and the margin of error around the sample mean could be quite large. \n",
    "\n",
    "The Fama—Macbeth methodology leverages the covariance of the factors with other assets to determine the factor premia. The second moment of asset returns is easier to estimate than the first moment, and obtaining more granular data improves estimation considerably, which is not true of mean estimation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Factor Exposures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can implement the first stage to obtain the 17 factor loading estimates as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:20.550415Z",
     "start_time": "2020-06-17T18:12:20.302417Z"
    }
   },
   "outputs": [],
   "source": [
    "betas = []\n",
    "for industry in ff_portfolio_data:\n",
    "    step1 = OLS(endog=ff_portfolio_data.loc[ff_factor_data.index, industry], \n",
    "                exog=add_constant(ff_factor_data)).fit()\n",
    "    betas.append(step1.params.drop('const'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:20.587754Z",
     "start_time": "2020-06-17T18:12:20.564914Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 17 entries, Food  to Other\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Mkt-RF  17 non-null     float64\n",
      " 1   SMB     17 non-null     float64\n",
      " 2   HML     17 non-null     float64\n",
      " 3   RMW     17 non-null     float64\n",
      " 4   CMA     17 non-null     float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 1.4+ KB\n"
     ]
    }
   ],
   "source": [
    "betas = pd.DataFrame(betas, \n",
    "                     columns=ff_factor_data.columns, \n",
    "                     index=ff_portfolio_data.columns)\n",
    "betas.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Risk Premia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the second stage, we run 96 regressions of the period returns for the cross section of portfolios on the factor loadings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:20.849956Z",
     "start_time": "2020-06-17T18:12:20.607394Z"
    }
   },
   "outputs": [],
   "source": [
    "lambdas = []\n",
    "for period in ff_portfolio_data.index:\n",
    "    step2 = OLS(endog=ff_portfolio_data.loc[period, betas.index], \n",
    "                exog=betas).fit()\n",
    "    lambdas.append(step2.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:20.911570Z",
     "start_time": "2020-06-17T18:12:20.851517Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "PeriodIndex: 95 entries, 2010-02 to 2017-12\n",
      "Freq: M\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Mkt-RF  95 non-null     float64\n",
      " 1   SMB     95 non-null     float64\n",
      " 2   HML     95 non-null     float64\n",
      " 3   RMW     95 non-null     float64\n",
      " 4   CMA     95 non-null     float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 10.2 KB\n"
     ]
    }
   ],
   "source": [
    "lambdas = pd.DataFrame(lambdas, \n",
    "                       index=ff_portfolio_data.index,\n",
    "                       columns=betas.columns.tolist())\n",
    "lambdas.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:31.435381Z",
     "start_time": "2020-06-17T18:12:30.802096Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lambdas.mean().sort_values().plot.barh(figsize=(12, 4))\n",
    "sns.despine()\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:21.339451Z",
     "start_time": "2020-06-17T18:12:21.333568Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mkt-RF    0.342748\n",
       "SMB      -0.011482\n",
       "HML      -0.262373\n",
       "RMW      -0.046212\n",
       "CMA      -0.175708\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = lambdas.mean().div(lambdas.std())\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:22.213549Z",
     "start_time": "2020-06-17T18:12:21.341315Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "window = 24  # months\n",
    "ax1 = plt.subplot2grid((1, 3), (0, 0))\n",
    "ax2 = plt.subplot2grid((1, 3), (0, 1), colspan=2)\n",
    "lambdas.mean().sort_values().plot.barh(ax=ax1)\n",
    "lambdas.rolling(window).mean().dropna().plot(lw=1,\n",
    "                                             figsize=(14, 5),\n",
    "                                             sharey=True,\n",
    "                                             ax=ax2)\n",
    "sns.despine()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:24.739887Z",
     "start_time": "2020-06-17T18:12:22.214983Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "window = 24  # months\n",
    "lambdas.rolling(window).mean().dropna().plot(lw=2,\n",
    "                                             figsize=(14, 7),\n",
    "                                             subplots=True,\n",
    "                                             sharey=True)\n",
    "sns.despine()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fama-Macbeth with the LinearModels library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear_models library extends statsmodels with various models for panel data and also implements the two-stage Fama—MacBeth procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:24.798752Z",
     "start_time": "2020-06-17T18:12:24.741596Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      LinearFactorModel Estimation Summary                      \n",
      "================================================================================\n",
      "No. Test Portfolios:                 17   R-squared:                      0.6889\n",
      "No. Factors:                          5   J-statistic:                    17.081\n",
      "No. Observations:                    95   P-value                         0.1466\n",
      "Date:                  Wed, Jun 17 2020   Distribution:                 chi2(12)\n",
      "Time:                          14:12:24                                         \n",
      "Cov. Estimator:                  robust                                         \n",
      "                                                                                \n",
      "                            Risk Premia Estimates                             \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "Mkt-RF         1.2294     0.4076     3.0161     0.0026      0.4305      2.0282\n",
      "SMB           -0.0452     0.8661    -0.0522     0.9584     -1.7427      1.6524\n",
      "HML           -1.0782     0.6886    -1.5658     0.1174     -2.4278      0.2714\n",
      "RMW           -0.1397     0.8304    -0.1682     0.8664     -1.7672      1.4879\n",
      "CMA           -0.6245     0.5075    -1.2305     0.2185     -1.6193      0.3702\n",
      "==============================================================================\n",
      "\n",
      "Covariance estimator:\n",
      "HeteroskedasticCovariance\n",
      "See full_summary for complete results\n"
     ]
    }
   ],
   "source": [
    "mod = LinearFactorModel(portfolios=ff_portfolio_data, \n",
    "                        factors=ff_factor_data)\n",
    "res = mod.fit()\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:24.931716Z",
     "start_time": "2020-06-17T18:12:24.800766Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      LinearFactorModel Estimation Summary                      \n",
      "================================================================================\n",
      "No. Test Portfolios:                 17   R-squared:                      0.6889\n",
      "No. Factors:                          5   J-statistic:                    17.081\n",
      "No. Observations:                    95   P-value                         0.1466\n",
      "Date:                  Wed, Jun 17 2020   Distribution:                 chi2(12)\n",
      "Time:                          14:12:24                                         \n",
      "Cov. Estimator:                  robust                                         \n",
      "                                                                                \n",
      "                            Risk Premia Estimates                             \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "Mkt-RF         1.2294     0.4076     3.0161     0.0026      0.4305      2.0282\n",
      "SMB           -0.0452     0.8661    -0.0522     0.9584     -1.7427      1.6524\n",
      "HML           -1.0782     0.6886    -1.5658     0.1174     -2.4278      0.2714\n",
      "RMW           -0.1397     0.8304    -0.1682     0.8664     -1.7672      1.4879\n",
      "CMA           -0.6245     0.5075    -1.2305     0.2185     -1.6193      0.3702\n",
      "\n",
      "\n",
      "                              Food  Coefficients                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "alpha          0.1945     0.2465     0.7893     0.4300     -0.2885      0.6776\n",
      "Mkt-RF         0.6805     0.0465     14.640     0.0000      0.5894      0.7716\n",
      "SMB           -0.3202     0.1114    -2.8747     1.9960     -0.5385     -0.1019\n",
      "HML           -0.3637     0.1425    -2.5521     1.9893     -0.6429     -0.0844\n",
      "RMW            0.2872     0.1238     2.3206     0.0203      0.0446      0.5298\n",
      "CMA            0.5111     0.1656     3.0866     0.0020      0.1866      0.8357\n",
      "\n",
      "\n",
      "                              Mines Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.6552     0.5410    -1.2112     1.7742     -1.7154      0.4051\n",
      "Mkt-RF         1.3028     0.1958     6.6531     0.0000      0.9190      1.6866\n",
      "SMB            0.1932     0.3387     0.5703     0.5685     -0.4707      0.8571\n",
      "HML            0.1731     0.3299     0.5248     0.5997     -0.4735      0.8197\n",
      "RMW            0.1854     0.4360     0.4251     0.6707     -0.6692      1.0400\n",
      "CMA            0.6152     0.5560     1.1065     0.2685     -0.4745      1.7048\n",
      "\n",
      "\n",
      "                              Oil   Coefficients                              \n",
      "==============================================================================\n",
      "alpha          0.1872     0.3456     0.5417     0.5880     -0.4901      0.8645\n",
      "Mkt-RF         1.0568     0.1030     10.260     0.0000      0.8549      1.2587\n",
      "SMB            0.1583     0.1959     0.8080     0.4191     -0.2256      0.5421\n",
      "HML            0.6357     0.2023     3.1421     0.0017      0.2392      1.0322\n",
      "RMW           -0.0678     0.2376    -0.2854     1.2247     -0.5335      0.3979\n",
      "CMA            0.3211     0.2877     1.1162     0.2643     -0.2427      0.8850\n",
      "\n",
      "\n",
      "                              Clths Coefficients                              \n",
      "==============================================================================\n",
      "alpha          0.1910     0.3392     0.5631     0.5734     -0.4738      0.8559\n",
      "Mkt-RF         0.9658     0.1194     8.0897     0.0000      0.7318      1.1998\n",
      "SMB            0.3483     0.1979     1.7601     0.0784     -0.0395      0.7361\n",
      "HML           -0.1947     0.2098    -0.9281     1.6466     -0.6060      0.2165\n",
      "RMW            0.6154     0.2742     2.2438     0.0248      0.0778      1.1529\n",
      "CMA            0.0613     0.3186     0.1925     0.8473     -0.5631      0.6857\n",
      "\n",
      "\n",
      "                              Durbl Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.1379     0.3199    -0.4311     1.3336     -0.7650      0.4891\n",
      "Mkt-RF         1.1750     0.0820     14.328     0.0000      1.0143      1.3357\n",
      "SMB            0.5540     0.1164     4.7596     0.0000      0.3258      0.7821\n",
      "HML            0.0557     0.1461     0.3810     0.7032     -0.2307      0.3421\n",
      "RMW            0.5825     0.1865     3.1230     0.0018      0.2169      0.9481\n",
      "CMA           -0.1023     0.2605    -0.3926     1.3054     -0.6128      0.4082\n",
      "\n",
      "\n",
      "                              Chems Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.2047     0.3294    -0.6215     1.4657     -0.8503      0.4409\n",
      "Mkt-RF         1.3556     0.1057     12.822     0.0000      1.1483      1.5628\n",
      "SMB            0.1854     0.1491     1.2438     0.2136     -0.1068      0.4776\n",
      "HML            0.2014     0.1458     1.3810     0.1673     -0.0844      0.4873\n",
      "RMW            0.2013     0.1962     1.0256     0.3051     -0.1834      0.5859\n",
      "CMA           -0.2493     0.2646    -0.9422     1.6539     -0.7679      0.2693\n",
      "\n",
      "\n",
      "                              Cnsum Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.0568     0.3516    -0.1614     1.1283     -0.7460      0.6324\n",
      "Mkt-RF         0.7597     0.0595     12.758     0.0000      0.6430      0.8764\n",
      "SMB           -0.3357     0.1005    -3.3385     1.9992     -0.5327     -0.1386\n",
      "HML           -0.5766     0.1254    -4.5972     2.0000     -0.8225     -0.3308\n",
      "RMW           -0.0573     0.1311    -0.4369     1.3378     -0.3142      0.1997\n",
      "CMA            0.5912     0.2270     2.6040     0.0092      0.1462      1.0361\n",
      "\n",
      "\n",
      "                              Cnstr Coefficients                              \n",
      "==============================================================================\n",
      "alpha          0.6238     0.4056     1.5381     0.1240     -0.1711      1.4187\n",
      "Mkt-RF         1.1169     0.0810     13.783     0.0000      0.9581      1.2757\n",
      "SMB            0.4488     0.1355     3.3113     0.0009      0.1832      0.7145\n",
      "HML            0.1087     0.1922     0.5656     0.5716     -0.2680      0.4854\n",
      "RMW            0.0112     0.2269     0.0496     0.9605     -0.4335      0.4560\n",
      "CMA            0.1269     0.2446     0.5190     0.6038     -0.3525      0.6064\n",
      "\n",
      "\n",
      "                              Steel Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.3524     0.4092    -0.8611     1.6108     -1.1543      0.4496\n",
      "Mkt-RF         1.4669     0.1377     10.653     0.0000      1.1970      1.7368\n",
      "SMB            0.4237     0.2559     1.6558     0.0978     -0.0778      0.9252\n",
      "HML            0.3869     0.2686     1.4408     0.1496     -0.1394      0.9133\n",
      "RMW            0.1577     0.3387     0.4656     0.6415     -0.5062      0.8215\n",
      "CMA            0.4894     0.4269     1.1465     0.2516     -0.3473      1.3261\n",
      "\n",
      "\n",
      "                              FabPr Coefficients                              \n",
      "==============================================================================\n",
      "alpha          0.2138     0.2937     0.7279     0.4666     -0.3619      0.7895\n",
      "Mkt-RF         1.0708     0.0726     14.744     0.0000      0.9285      1.2132\n",
      "SMB            0.4777     0.0978     4.8828     0.0000      0.2860      0.6695\n",
      "HML           -0.0413     0.1107    -0.3733     1.2911     -0.2583      0.1756\n",
      "RMW            0.2073     0.1408     1.4721     0.1410     -0.0687      0.4832\n",
      "CMA            0.2044     0.1875     1.0900     0.2757     -0.1631      0.5718\n",
      "\n",
      "\n",
      "                              Machn Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.3277     0.2648    -1.2373     1.7840     -0.8468      0.1914\n",
      "Mkt-RF         1.1909     0.0579     20.577     0.0000      1.0774      1.3043\n",
      "SMB            0.1919     0.1075     1.7840     0.0744     -0.0189      0.4026\n",
      "HML            0.0290     0.1051     0.2761     0.7825     -0.1770      0.2350\n",
      "RMW            0.0855     0.1608     0.5317     0.5949     -0.2297      0.4006\n",
      "CMA           -0.3730     0.1762    -2.1171     1.9657     -0.7182     -0.0277\n",
      "\n",
      "\n",
      "                              Cars  Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.0971     0.4014    -0.2419     1.1911     -0.8837      0.6896\n",
      "Mkt-RF         1.1920     0.0992     12.019     0.0000      0.9976      1.3863\n",
      "SMB            0.6099     0.1289     4.7302     0.0000      0.3572      0.8626\n",
      "HML            0.0395     0.1801     0.2195     0.8263     -0.3135      0.3925\n",
      "RMW            0.0840     0.2162     0.3885     0.6977     -0.3397      0.5076\n",
      "CMA           -0.0121     0.2990    -0.0404     1.0322     -0.5980      0.5739\n",
      "\n",
      "\n",
      "                              Trans Coefficients                              \n",
      "==============================================================================\n",
      "alpha          0.5045     0.3353     1.5045     0.1325     -0.1527      1.1617\n",
      "Mkt-RF         1.0229     0.0511     20.031     0.0000      0.9228      1.1230\n",
      "SMB            0.2560     0.1011     2.5315     0.0114      0.0578      0.4542\n",
      "HML            0.0271     0.1203     0.2255     0.8216     -0.2087      0.2630\n",
      "RMW            0.4100     0.1627     2.5195     0.0118      0.0910      0.7289\n",
      "CMA            0.2571     0.1972     1.3034     0.1924     -0.1295      0.6436\n",
      "\n",
      "\n",
      "                              Utils Coefficients                              \n",
      "==============================================================================\n",
      "alpha          0.3702     0.3098     1.1949     0.2321     -0.2370      0.9774\n",
      "Mkt-RF         0.4943     0.0898     5.5048     0.0000      0.3183      0.6703\n",
      "SMB           -0.2607     0.1556    -1.6760     1.9063     -0.5656      0.0442\n",
      "HML           -0.3138     0.1817    -1.7270     1.9158     -0.6700      0.0423\n",
      "RMW            0.1918     0.1931     0.9930     0.3207     -0.1867      0.5703\n",
      "CMA            0.5656     0.3043     1.8585     0.0631     -0.0309      1.1620\n",
      "\n",
      "\n",
      "                              Rtail Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.0131     0.2597    -0.0504     1.0402     -0.5222      0.4960\n",
      "Mkt-RF         0.8990     0.0687     13.080     0.0000      0.7643      1.0338\n",
      "SMB            0.1193     0.1008     1.1830     0.2368     -0.0783      0.3169\n",
      "HML           -0.3969     0.1270    -3.1259     1.9982     -0.6457     -0.1480\n",
      "RMW            0.6846     0.1620     4.2270     0.0000      0.3672      1.0020\n",
      "CMA            0.2530     0.1744     1.4505     0.1469     -0.0889      0.5949\n",
      "\n",
      "\n",
      "                              Finan Coefficients                              \n",
      "==============================================================================\n",
      "alpha          0.3660     0.3752     0.9753     0.3294     -0.3695      1.1014\n",
      "Mkt-RF         1.0636     0.0431     24.663     0.0000      0.9791      1.1481\n",
      "SMB            0.0784     0.0848     0.9245     0.3552     -0.0879      0.2447\n",
      "HML            0.7566     0.0876     8.6346     0.0000      0.5848      0.9283\n",
      "RMW           -0.4360     0.1060    -4.1139     2.0000     -0.6437     -0.2283\n",
      "CMA           -0.5669     0.1145    -4.9525     2.0000     -0.7912     -0.3425\n",
      "\n",
      "\n",
      "                              Other Coefficients                              \n",
      "==============================================================================\n",
      "alpha         -0.1515     0.2289    -0.6619     1.4919     -0.6002      0.2972\n",
      "Mkt-RF         1.0410     0.0247     42.160     0.0000      0.9926      1.0894\n",
      "SMB           -0.1154     0.0397    -2.9037     1.9963     -0.1932     -0.0375\n",
      "HML           -0.1958     0.0385    -5.0838     2.0000     -0.2713     -0.1203\n",
      "RMW           -0.0669     0.0625    -1.0700     1.7154     -0.1894      0.0556\n",
      "CMA            0.0125     0.0658     0.1894     0.8498     -0.1166      0.1415\n",
      "==============================================================================\n",
      "\n",
      "Covariance estimator:\n",
      "HeteroskedasticCovariance\n",
      "See full_summary for complete results\n"
     ]
    }
   ],
   "source": [
    "print(res.full_summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This provides us with the same result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-17T18:12:24.942021Z",
     "start_time": "2020-06-17T18:12:24.933661Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mkt-RF    1.229358\n",
       "SMB      -0.045169\n",
       "HML      -1.078180\n",
       "RMW      -0.139674\n",
       "CMA      -0.624548\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambdas.mean()"
   ]
  }
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